1Dalhousie University, Department of Oceanography, Halifax, Canada
2Department de Biologié, Université Laval, Québec, Québec, Canada
3Demersal and Benthic Sciences Division, Maurice-Lamontagne Institute, Fisheries and Oceans Canada, Mont-Joli, Québec, Canada
4Arctic and Aquatic Research Division, Fisheries and Oceans Canada, Winnipeg, Manitoba, Canada
5Institute of Marine Research, His, Norway
6Center of Earth Observation Science, University of Manitoba, Winnipeg, Manitoba, Canada
7Centre for Arctic Knowledge and Exploration, Canadian Museum of Nature, Ottawa, ON, Canada

Introduction
Current knowledge
Present
- Evidence suggests that many Arctic coasts should support seaweed
- In Canada, kelp has been reported and documented along Arctic and subarctic coastlines
- However, baseline measures of the extent of kelp communities are missing in much of the region
Future
- Rapid environmental changes, such as declining sea ice, increased ocean temperatures, and freshwater inputs are occurring along Canadian coasts
- Research suggests northern expansion of kelp forests with climate change
- Therefore, the relationships between environmental factors and the presence of kelp forests in the Canadian Arctic are critical to understand
Existing database

ArcticKelp project
- This dive research conducted throughout the Canadian Arctic in 2014 - 2019
- 5 - 20 m photograph quadrats
Campaigns

Mean cover

Questions
- Is it possible to model the distribution (suitability + abundance) of different functional groups of kelps in the Arctic given our current knowledge?
- Total kelp cover
- Laminariales (Laminaria sp. + Sacharina sp.)
- Agarum
- Alaria
- How accurate are the models?
- Which environmental variables are the most important?
- What might future distributions look like?
Methods
Data

(Assis et al., 2018; Tyberghein et al., 2012)
Bio-ORACLE
- Geophysical, biotic, and abiotic environmental variables
- Collection from many different datasets
- Surface and benthic coverage
- Data from 2000 - 2014 for most
- Single values per pixel; min, mean, max, and range for most
- 5 arcdegree spatial resolution (~9.2 km at the equator)
Variables (32)
- Temperature
- Salinity
- Ice thickness (surface only)
- Current velocity
- Photosynthetically active radiation (PAR; surface only)
- Dissolve oxygen
- Iron
- Nitrate
- Phosphate
Final variables (8)
- Bottom temperature; long-term minimum
- Bottom temperature; long-term maximum
- Surface temperature; long-term maximum
- Bottom salinity; long-term maximum
- Ice thickness; long-term minimum
- Bottom iron; long-term maximum
- Bottom phosphate; long-term maximum
- Bottom current velocity; long-term minimum
Future variables (6)
- Bottom temperature; long-term minimum
- Bottom temperature; long-term maximum
- Surface temperature; long-term maximum
- Bottom salinity; long-term maximum
- Ice thickness; long-term minimum
- Bottom current velocity; long-term minimum
Ensemble model (suitability)
- Ensemble performed with default BIOMOD2 settings (Thuiller et al., 2020)
- Models: MAXENT (Phillips), GLM, ANN, RF, GAM (Goldsmit et al., 2020)
- Random-pseudo absence (PA); 1000 points; 5 repetitions
- 70/30 train test split
- Modeled for entire Arctic ecoregion
- Results cropped to Eastern Canadian Arctic
Random forest model (abundance)
- 200 trees; 1000 repetitions
- 70/30 train test split
- Modeled only for Eastern Canadian Arctic
Results
Ensemble
Random Forest
Confidence
Laminariales

Agarum

Alaria

Total cover

Top variables
Laminariales
|
Data layer
|
% Inc. MSE
|
|
Latitude
|
40
|
|
Longitude
|
27
|
|
Photosynthetically available radiation (mean)
|
23
|
|
Ice fraction
|
0
|
|
Chlorophyll concentration (mean at min depth)
|
0
|
|
Evap minus Precip over ocean
|
0
|
Agarum
|
Data layer
|
% Inc. MSE
|
|
Ice thickness (cell average)
|
66
|
|
Light at bottom (mean at min depth)
|
53
|
|
Iron concentration (mean at min depth)
|
46
|
|
Net Downward Heat Flux
|
0
|
|
shear
|
0
|
|
total flux at ocean surface
|
0
|
Alaria
|
Data layer
|
% Inc. MSE
|
|
total flux at ocean surface
|
8
|
|
non-solar heat flux at ocean surface
|
8
|
|
Sea Water Salinity
|
7
|
|
Light at bottom (mean at min depth)
|
0
|
|
kinetic energy
|
0
|
|
daily dynamic ice prod.
|
0
|
Total kelp
|
Data layer
|
% Inc. MSE
|
|
Sea water temperature (mean at min depth)
|
92
|
|
Dissolved oxygen concentration (mean at min depth)
|
86
|
|
Ice divergence
|
80
|
|
Sea Water Y Velocity
|
0
|
|
kinetic energy
|
0
|
|
heat fluxes causing bottom ice melt
|
0
|
Projections
- Note that the colour scales are not the same between figures
Laminariales

Agarum

Alaria

Total cover

Linear regression
Conclusions
Ensemble
RF
Contrast
Why
There should be quite a lot of kelp in the Arctic
There are different spatial projections for different groups
Alaria projections are likely incorrect and require more data
These projections provide a good platform for deciding future sampling locations
Further work
Acknowledgements
- This research was undertaken thanks in part to funding from the Canada First Research Excellence Fund, through the Ocean Frontier Institute.

References
Assis, J., Tyberghein, L., Bosch, S., Verbruggen, H., Serrão, E. A., and De Clerck, O. (2018). Bio-oracle v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography 27, 277–284.
Goldsmit, J., McKindsey, C. W., Schlegel, R. W., Stewart, D. B., Archambault, P., and Howland, K. L. (2020). What and where? Predicting invasion hotspots in the arctic marine realm. Global change biology 26, 4752–4771.
Tyberghein, L., Verbruggen, H., Pauly, K., Troupin, C., Mineur, F., and De Clerck, O. (2012). Bio-oracle: A global environmental dataset for marine species distribution modelling. Global ecology and biogeography 21, 272–281.